GAMIC#
[1]:
import xarray as xr
from open_radar_data import DATASETS
import xradar as xd
Download#
Fetching GAMIC radar data file from open-radar-data repository.
[2]:
filename = DATASETS.fetch("DWD-Vol-2_99999_20180601054047_00.h5")
Downloading file 'DWD-Vol-2_99999_20180601054047_00.h5' from 'https://github.com/openradar/open-radar-data/raw/main/data/DWD-Vol-2_99999_20180601054047_00.h5' to '/home/docs/.cache/open-radar-data'.
xr.open_dataset#
Making use of the xarray gamic backend. We also need to provide the group. Note, that we are using CfRadial2 group access pattern.
[3]:
ds = xr.open_dataset(filename, group="sweep_9", engine="gamic")
display(ds)
<xarray.Dataset> Size: 17MB
Dimensions: (azimuth: 360, range: 1000)
Coordinates:
elevation (azimuth) float64 3kB ...
time (azimuth) datetime64[ns] 3kB ...
* range (range) float32 4kB 75.0 225.0 ... 1.498e+05 1.499e+05
longitude float64 8B ...
latitude float64 8B ...
altitude float64 8B ...
* azimuth (azimuth) float64 3kB 0.5109 1.519 2.519 ... 358.5 359.5
Data variables: (12/17)
DBZH (azimuth, range) float32 1MB ...
DBZV (azimuth, range) float32 1MB ...
KDP (azimuth, range) float32 1MB ...
RHOHV (azimuth, range) float32 1MB ...
DBTH (azimuth, range) float32 1MB ...
DBTV (azimuth, range) float32 1MB ...
... ...
PHIDP (azimuth, range) float32 1MB ...
sweep_mode <U20 80B ...
sweep_number int64 8B ...
prt_mode <U7 28B ...
follow_mode <U7 28B ...
sweep_fixed_angle float64 8B ...
Attributes:
source: gamic
ant_gain_h: 43
ant_gain_v: 43
noise_power_h: -3.40133
noise_power_v: -3.13518
rx_loss_h: 3
rx_loss_v: 3Plot Time vs. Azimuth#
[4]:
ds.azimuth.plot()
[4]:
[<matplotlib.lines.Line2D at 0x7f5d25eacfb0>]
Plot Range vs. Time#
[5]:
ds.DBZH.plot()
[5]:
<matplotlib.collections.QuadMesh at 0x7f5d25cb0bf0>
Plot Range vs. Azimuth#
We need to sort by azimuth and specify the y-coordinate.
[6]:
ds.DBZH.sortby("azimuth").plot(y="azimuth")
[6]:
<matplotlib.collections.QuadMesh at 0x7f5d1dbcfc50>
backend_kwargs#
Beside first_dim there are several additional backend_kwargs for the odim backend, which handle different aspects of angle alignment. This comes into play, when azimuth and/or elevation arrays are not evenly spacend and other issues.
[7]:
help(xd.io.GamicBackendEntrypoint)
Help on class GamicBackendEntrypoint in module xradar.io.backends.gamic:
class GamicBackendEntrypoint(xarray.backends.common.BackendEntrypoint)
| Xarray BackendEntrypoint for GAMIC data.
|
| Keyword Arguments
| -----------------
| first_dim : str
| Can be ``time`` or ``auto`` first dimension. If set to ``auto``,
| first dimension will be either ``azimuth`` or ``elevation`` depending on
| type of sweep. Defaults to ``auto``.
| reindex_angle : bool or dict
| Defaults to False, no reindexing. Given dict should contain the kwargs to
| reindex_angle. Only invoked if `decode_coord=True`.
| fix_second_angle : bool
| For PPI only. If True, fixes erroneous second angle data. Defaults to ``False``.
| site_coords : bool
| Attach radar site-coordinates to Dataset, defaults to ``True``.
| kwargs : dict
| Additional kwargs are fed to :py:func:`xarray.open_dataset`.
|
| Method resolution order:
| GamicBackendEntrypoint
| xarray.backends.common.BackendEntrypoint
| builtins.object
|
| Methods defined here:
|
| open_dataset(self, filename_or_obj, *, mask_and_scale=True, decode_times=True, concat_characters=True, decode_coords=True, drop_variables=None, use_cftime=None, decode_timedelta=None, format=None, group='sweep_0', invalid_netcdf=None, phony_dims='access', decode_vlen_strings=True, first_dim='auto', reindex_angle=False, fix_second_angle=False, site_coords=True)
| Backend open_dataset method used by Xarray in :py:func:`~xarray.open_dataset`.
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __annotations__ = {}
|
| description = 'Open GAMIC HDF5 (.h5, .hdf5, .mvol) using h5netcdf in X...
|
| open_dataset_parameters = ('filename_or_obj', 'mask_and_scale', 'decod...
|
| url = 'https://xradar.rtfd.io/en/latest/io.html#gamic-hdf5'
|
| ----------------------------------------------------------------------
| Methods inherited from xarray.backends.common.BackendEntrypoint:
|
| __repr__(self) -> 'str'
| Return repr(self).
|
| guess_can_open(self, filename_or_obj: 'str | os.PathLike[Any] | ReadBuffer | AbstractDataStore') -> 'bool'
| Backend open_dataset method used by Xarray in :py:func:`~xarray.open_dataset`.
|
| open_datatree(self, filename_or_obj: 'str | os.PathLike[Any] | ReadBuffer | AbstractDataStore', *, drop_variables: 'str | Iterable[str] | None' = None) -> 'DataTree'
| Backend open_datatree method used by Xarray in :py:func:`~xarray.open_datatree`.
|
| open_groups_as_dict(self, filename_or_obj: 'str | os.PathLike[Any] | ReadBuffer | AbstractDataStore', *, drop_variables: 'str | Iterable[str] | None' = None) -> 'dict[str, Dataset]'
| Opens a dictionary mapping from group names to Datasets.
|
| Called by :py:func:`~xarray.open_groups`.
| This function exists to provide a universal way to open all groups in a file,
| before applying any additional consistency checks or requirements necessary
| to create a `DataTree` object (typically done using :py:meth:`~xarray.DataTree.from_dict`).
|
| ----------------------------------------------------------------------
| Data descriptors inherited from xarray.backends.common.BackendEntrypoint:
|
| __dict__
| dictionary for instance variables
|
| __weakref__
| list of weak references to the object
[8]:
ds = xr.open_dataset(filename, group="sweep_9", engine="gamic", first_dim="time")
display(ds)
<xarray.Dataset> Size: 17MB
Dimensions: (time: 360, range: 1000)
Coordinates:
elevation (time) float64 3kB ...
* time (time) datetime64[ns] 3kB 2018-06-01T05:43:40.504000 ....
* range (range) float32 4kB 75.0 225.0 ... 1.498e+05 1.499e+05
longitude float64 8B ...
latitude float64 8B ...
altitude float64 8B ...
azimuth (time) float64 3kB ...
Data variables: (12/17)
DBZH (time, range) float32 1MB ...
DBZV (time, range) float32 1MB ...
KDP (time, range) float32 1MB ...
RHOHV (time, range) float32 1MB ...
DBTH (time, range) float32 1MB ...
DBTV (time, range) float32 1MB ...
... ...
PHIDP (time, range) float32 1MB ...
sweep_mode <U20 80B ...
sweep_number int64 8B ...
prt_mode <U7 28B ...
follow_mode <U7 28B ...
sweep_fixed_angle float64 8B ...
Attributes:
source: gamic
ant_gain_h: 43
ant_gain_v: 43
noise_power_h: -3.40133
noise_power_v: -3.13518
rx_loss_h: 3
rx_loss_v: 3open_odim_datatree#
The same works analoguous with the datatree loader. But additionally we can provide a sweep string, number or list.
[9]:
help(xd.io.open_gamic_datatree)
Help on function open_gamic_datatree in module xradar.io.backends.gamic:
open_gamic_datatree(filename_or_obj, **kwargs)
Open GAMIC HDF5 dataset as :py:class:`xarray.DataTree`.
Parameters
----------
filename_or_obj : str, Path, file-like or DataStore
Strings and Path objects are interpreted as a path to a local or remote
radar file
Keyword Arguments
-----------------
sweep : int, list of int, optional
Sweep number(s) to extract, default to first sweep. If None, all sweeps are
extracted into a list.
first_dim : str
Can be ``time`` or ``auto`` first dimension. If set to ``auto``,
first dimension will be either ``azimuth`` or ``elevation`` depending on
type of sweep. Defaults to ``auto``.
reindex_angle : bool or dict
Defaults to False, no reindexing. Given dict should contain the kwargs to
reindex_angle. Only invoked if `decode_coord=True`.
fix_second_angle : bool
If True, fixes erroneous second angle data. Defaults to ``False``.
site_coords : bool
Attach radar site-coordinates to Dataset, defaults to ``True``.
kwargs : dict
Additional kwargs are fed to :py:func:`xarray.open_dataset`.
Returns
-------
dtree: xarray.DataTree
DataTree
[10]:
dtree = xd.io.open_gamic_datatree(filename, sweep=8)
display(dtree)
<xarray.DatasetView> Size: 248B
Dimensions: (sweep: 1)
Dimensions without coordinates: sweep
Data variables:
volume_number int64 8B 0
platform_type <U5 20B 'fixed'
instrument_type <U5 20B 'radar'
time_coverage_start <U20 80B '2018-06-01T05:43:08Z'
time_coverage_end <U20 80B '2018-06-01T05:43:38Z'
longitude float64 8B 6.457
altitude float64 8B 310.0
latitude float64 8B 50.93
sweep_group_name (sweep) int64 8B 8
sweep_fixed_angle (sweep) float64 8B 1.7
Attributes:
Conventions: None
instrument_name: None
version: None
title: None
institution: None
references: None
source: gamic
history: None
comment: im/exported using xradarPlot Sweep Range vs. Time#
[11]:
dtree["sweep_0"].ds.DBZH.sortby("time").plot(y="time")
[11]:
<matplotlib.collections.QuadMesh at 0x7f5d1dbbaf60>
Plot Sweep Range vs. Azimuth#
[12]:
dtree["sweep_0"].ds.DBZH.plot()
[12]:
<matplotlib.collections.QuadMesh at 0x7f5d1daa5070>
[13]:
dtree = xd.io.open_gamic_datatree(filename, sweep="sweep_8")
display(dtree)
<xarray.DatasetView> Size: 248B
Dimensions: (sweep: 1)
Dimensions without coordinates: sweep
Data variables:
volume_number int64 8B 0
platform_type <U5 20B 'fixed'
instrument_type <U5 20B 'radar'
time_coverage_start <U20 80B '2018-06-01T05:43:08Z'
time_coverage_end <U20 80B '2018-06-01T05:43:38Z'
longitude float64 8B 6.457
altitude float64 8B 310.0
latitude float64 8B 50.93
sweep_group_name (sweep) int64 8B 8
sweep_fixed_angle (sweep) float64 8B 1.7
Attributes:
Conventions: None
instrument_name: None
version: None
title: None
institution: None
references: None
source: gamic
history: None
comment: im/exported using xradar[14]:
dtree = xd.io.open_gamic_datatree(filename, sweep=[0, 1, 8])
display(dtree)
<xarray.DatasetView> Size: 280B
Dimensions: (sweep: 3)
Dimensions without coordinates: sweep
Data variables:
volume_number int64 8B 0
platform_type <U5 20B 'fixed'
instrument_type <U5 20B 'radar'
time_coverage_start <U20 80B '2018-06-01T05:40:47Z'
time_coverage_end <U20 80B '2018-06-01T05:43:38Z'
longitude float64 8B 6.457
altitude float64 8B 310.0
latitude float64 8B 50.93
sweep_group_name (sweep) int64 24B 0 1 8
sweep_fixed_angle (sweep) float64 24B 28.0 18.0 1.7
Attributes:
Conventions: None
instrument_name: None
version: None
title: None
institution: None
references: None
source: gamic
history: None
comment: im/exported using xradar[15]:
dtree = xd.io.open_gamic_datatree(filename, sweep=["sweep_1", "sweep_2", "sweep_8"])
display(dtree)
<xarray.DatasetView> Size: 280B
Dimensions: (sweep: 3)
Dimensions without coordinates: sweep
Data variables:
volume_number int64 8B 0
platform_type <U5 20B 'fixed'
instrument_type <U5 20B 'radar'
time_coverage_start <U20 80B '2018-06-01T05:41:01Z'
time_coverage_end <U20 80B '2018-06-01T05:43:38Z'
longitude float64 8B 6.457
altitude float64 8B 310.0
latitude float64 8B 50.93
sweep_group_name (sweep) int64 24B 1 2 8
sweep_fixed_angle (sweep) float64 24B 18.0 14.0 1.7
Attributes:
Conventions: None
instrument_name: None
version: None
title: None
institution: None
references: None
source: gamic
history: None
comment: im/exported using xradar